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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Agentic Experiences | 25% | - Agentic Readiness
|
| Topic 2: Embedding, Cross-Cloud, & Interoperability | 20% | - Cross-Platform Workflows
|
| Topic 3: Managing Workspaces & Orgs | 10% | - Asset Management and Sharing
|
| Topic 4: Visualizations & Dashboards | 15% | - Visualization and Dashboard Design
|
| Topic 5: Basic Setup & Admin | 10% | - Agentic Analytics
|
| Topic 6: Data Setup | 20% | - Semantic Models
|
もし、あなたもAnalytics-Con-202試験に合格したいです。しかし、どんな資料を選択したらいいですか？お勧めしたいのはAnalytics-Con-202試験問題集です。購入する前に、SalesforceのウエブサイトでAnalytics-Con-202試験問題集のデモをダウンロードしてみると、あなたはきっとAnalytics-Con-202試験問題集に魅了されます。
質問 # 85
The client wants AI-generated insights about Gross Margin % in Tableau Next. Where is the best place to define this context for the agent?
正解：C
質問 # 86
A Tableau Next Consultant has enabled Acceleration for a connected data source, which is configured to refresh data as frequently as every 15 minutes. However, records that have been deleted from the source system are still appearing in Tableau Next 30 minutes later. What is the most likely reason for this behavior?
正解：B
解説：
When Acceleration for Data Connections is enabled, Tableau Next temporarily caches external-source data in Data 360 to reduce runtime latency and improve query performance. Two refresh approaches are available:
Full Refresh and Incremental Refresh. Salesforce explicitly states that an incremental refresh processes records that have been added or changed since the previous refresh but does not update deleted data.
Therefore, records removed from the source can remain present in the accelerated cache even after several incremental refresh cycles.
This explains why the 15-minute frequency does not solve the issue. Frequency controls how often incremental processing occurs; it does not change what the incremental algorithm processes. Option A incorrectly attributes the behavior to ordinary cache latency. Option C is also too broad because acceleration itself supports deletion synchronization when a Full Refresh is used. A full refresh removes the existing cached dataset and replaces it with the current source dataset.
References/Topics: Data Setup - > Data Connections - > Acceleration - > Full versus Incremental Refresh -
> Cache Refresh Method.
質問 # 87
The client wants AI-generated insights about Gross Margin % in Tableau Next. Where is the best place to define this context for the agent?
正解：C
解説：
For agent-specific business context about how Gross Margin % should be understood or interpreted, Business Preferences are the best choice. Salesforce uses Business Preferences to add organization-specific knowledge and instructions to a semantic model so Tableau Agent can interpret users' analytical questions according to the organization's terminology and business logic.
A calculated field is appropriate when Gross Margin % itself must be mathematically derived-for example, (Revenue - Cost) / Revenue. However, the question does not ask where to implement the mathematical calculation; it asks where to define context for AI-generated insights. Contextual instructions, such as what
"healthy gross margin" means or which business interpretation to apply, belong in Business Preferences.
A dashboard-scoped calculated field is even less suitable because it confines logic to a specific downstream analytical artifact instead of enriching the governed semantic context used by Tableau Agent across conversational experiences.
Salesforce's broader semantic architecture reinforces this separation: semantic models provide standardized business logic and definitions, while Business Preferences augment those models with business-specific interpretive knowledge for conversational analytics.
References/Topics: Agentic Experiences - > Business Preferences - > Tableau Agent Context - > Semantic Model Enrichment.
質問 # 88
A Tableau Next Consultant receives a request from a business user to help troubleshoot an issue with setting data alerts using Tableau Agent. The consultant verifies that the business user has been granted a Tableau Next Consumer license and is accessing Tableau Agent from a page with Tableau Next on it. Which reason explains why the business user is unable to set the alert?
正解：C
解説：
The limiting factor in this scenario is the user's license level. Setting proactive data alerts through Tableau Agent is an authoring-oriented capability that requires Tableau Next Creator access. A Consumer user can view and interact with published analytical content, but the Consumer persona does not receive the full set of creation and configuration capabilities needed to define alert conditions.
The fallback semantic model in option B is used when Tableau Agent lacks clear analytical context, such as when a user invokes the agent from a surface that does not already identify the relevant Tableau Next asset.
Here, the user is explicitly accessing Tableau Agent from a page that contains Tableau Next content, so lack of fallback scoping is not the primary issue. Option A can prevent analysis of a particular metric, but the question establishes a broader inability to set the alert and emphasizes that the user has a Consumer license.
The appropriate remediation is therefore to use the Creator-level entitlement for users who must configure proactive alerts. This preserves the distinction between consumption permissions and analytical creation
/configuration permissions.
References/Topics: Agentic Experiences - > Tableau Agent - > Proactive Data Alerts - > Creator Permissions.
質問 # 89
A Tableau Next Consultant is helping a customer improve their Tableau Agent experience for trending questions. Business users frequently ask questions like, "How has customer satisfaction trended this quarter compared to last?" but results are inconsistent. What is the most effective enhancement to the semantic model to improve these responses?
正解：B
解説：
The most effective improvement is to ensure that time dimensions and metric semantics are clearly and correctly defined. Tableau Agent's Data Analysis capability natively supports trend analysis, period-over- period comparisons, and date filtering, including year, quarter, month, week, day, fiscal periods, and relative date ranges.
For a question such as "How has customer satisfaction trended this quarter compared to last?", Tableau Agent must identify the correct customer-satisfaction measure or metric, understand its business definition, and resolve which date dimension establishes the comparison period. Clear metric descriptions and correct temporal metadata therefore materially improve query generation and response consistency.
Option A could create redundant semantic logic because Tableau Agent already supports period-over-period reasoning natively. A dedicated "Quarter over Quarter" calculated field is unnecessary unless there is genuinely specialized business logic that differs from the standard comparison. Option C is also inappropriate:
Salesforce already provides trend and period-over-period capabilities through the Data Analysis subagent.
Well-designed semantic models-not unnecessary proliferation of specialized agents-are the primary mechanism for improving grounded conversational analytics.
References/Topics: Agentic Experiences - > Tableau Agent - > Trend Analysis - > Period-over-Period Questions - > Semantic Model AI Readiness.
質問 # 90
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Analytics-Con-202試験トレーニングにより、最短時間で試験に合格することができます。十分な時間がない場合、Analytics-Con-202学習教材は本当に良い選択です。学習の過程で、Analytics-Con-202学習教材も効率を改善できます。学習する時間が足りない場合は、Analytics-Con-202テストガイドが空き時間を最大限に活用します。 Analytics-Con-202学習質問に合わせた専門家は、あなたに非常に適している必要があります。プロセスをより深く理解できます。すべての時間を効率的に使用して、私を信じて、あなたはあなたの夢を実現します。
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